Dataset for "Simulations of the impact of CCN and INP perturbations on the microphysics and radar reflectivity factor of stratiform mixed-phase clouds" by Lee et al. (2024)

Atmos. Chem. Phys., 24, 5737–5756, 2024 https://doi.org/10.5194/acp-24-5737-2024 © Author(s) 2024. This work is distributed under the Creative Commons Attribution 4.0 License. Research article Simulations of the impact of cloud condensation nuclei and ice-nucleating particles perturbations on the microphysics and radar reflectivity factor of stratiform mixed-phase clouds Junghwa Lee1, Patric Seifert1, Tempei Hashino2, Maximilian Maahn3, Fabian Senf1, and Oswald Knoth1 1Leibniz Institute for Tropospheric Research (TROPOS), Leipzig, Germany 2School of Environmental Science and Engineering, Kochi University of Technology, Kami, Japan 3Leipzig Institute of Meteorology (LIM), Leipzig University, Leipzig, Germany Correspondence: Junghwa Lee ([email protected]) Received: 18 August 2023 – Discussion started: 24 August 2023 Revised: 23 February 2024 – Accepted: 24 March 2024 – Published: 21 May 2024 Abstract. In this research, we delve into the influence of cloud condensation nuclei (CCN) and ice-nucleating particle (INP) concentrations on the morphology and abundance of ice particles in mixed-phase clouds, emphasizing the consequential impact of ice particle shape, number, and size on cloud dynamics and microphysics. Leveraging the synergy of the Advanced Microphysics Prediction System (AMPS) and the Kinematic Driver (KiD) model, we conducted simulations to capture cloud microphysics across diverse CCN and INP concentrations. The Passive and Active Microwave radiative TRAnsfer (PAMTRA) radar forward simulator further augmented our study, offering insights into how the concentrations of CCN and INPs affect radar reflectivities. Our experimental framework encompassed CCN concentrations ranging from 10 to 5000 cm−3and INP concentrations from 0.001 to 10 L−1. Central to our findings is the observation that higher INP concentrations yield smaller ice particles, while an increase in CCN concentrations leads to a subtle growth in their dimensions. Consistent with existing literature, our results spotlight oblate-like crystals as dominant between temperatures of −20 and −16 °C. Notably, high-INP scenarios unveiled a significant prevalence of irregular polycrystals. The aspect ratio (AR) of ice particles exhibited a decline with the rise in both CCN and INP concentrations, highlighting the nuanced interrelation between CCN levels and ice particle shape, especially its ramifications on the riming mechanism. The forward-simulated radar reflectivities, spanning from −11.83 dBZ (low INP, 0.001 L−1) to 4.65 dBZ (high INP, 10 L−1), elucidate the complex dynamics between CCN and INPs in determining mixed-phase cloud characteristics. Comparable differences in radar reflectivity were also reported from observational studies of stratiform mixed-phase clouds in contrasting aerosol environments. Our meticulous analysis of KiD-AMPS simulation outputs, coupled with insights into aerosol-driven microphysical changes, thus underscores the significance of this study in refining our ability to understand and interpret observations and climate projections. Published by Copernicus Publications on behalf of the European Geosciences Union. 5738 J. Lee et al.: Simulations of the impact of CCN and INP perturbations 1 Introduction Clouds are still one of the most uncertain components of the global atmosphere system (Bony et al., 2015). Their formation and evolution occur on various spatio-temporal scales, which makes it virtually impossible to tackle them with single, unified observational or simulation approaches (Kahn et al., 2023). Single sub-processes are studied individually and will only in a later stage be the basis for an improved comprehensive understanding. Important components of a cloud’s life cycle are, e.g., the cloud formation and the subsequent transitions from the liquid to the ice phase. The presence of the ice phase is an essential prerequisite for the production of adequate amounts of precipitation in most regions on Earth (Mülmenstädt et al., 2015). Nevertheless, for the initial formation of cloud droplets, as well as for the formation of ice crystals down to temperatures of −38 °C, aerosol particles are required for the phase transition by providing a reservoir of either cloud condensation nuclei (CCN) or ice-nucleating particles (INPs), respectively (Morrison et al., 2012; Hoose and Möhler, 2012). The interplay of the abundance of CCN and INPs and the cloud evolution is an important pathway of aerosol–cloud interaction. Perturbations in the concentration and type of CCN or INPs can in particular potentially influence the formation and evolution of ice particles in mixed-phase clouds. There are strong indications given by both observations and modeling approaches that INP and CCN perturbations do have a considerable impact on the mixed-phase cloud formation and evolution. Seifert et al. (2010) revealed increased fractions of ice-containing clouds in dust-laden cloud environments over central Europe. On a global scale, these findings were confirmed, e.g., by Zhang et al. (2018), using observations from the spaceborne A-Train satellite constellation. Hemispheric contrasts in mixed-phase clouds and their relationship to cloud turbulence and aerosol load were investigated in detail by Radenz et al. (2021), who also concluded that a measurable impact of aerosol on mixed-phase cloud formation exists. Seifert et al. (2012) revealed considerable impacts of a dust event on the simulation of clouds and precipitation patterns over Germany. Similar effects were identified in a European-scale approach by Barthlott and Hoose (2018). Fan et al. (2014) performed spectral bin simulations to investigate the influence of CCN and INPs on precipitation in two distinct mixed-phase orographic cloud scenarios characterized by different cloud temperatures. The study revealed varying degrees of significance regarding the impacts of CCN and INPs on precipitation, with the INPs exhibiting a more pronounced effect in both cases. Furthermore, Fan et al. (2017) conducted a sensitivity analysis where they systematically varied the concentrations of CCN and INP proxies across a wide range, spanning from extremely low to extremely high concentrations, employing spectral bin modeling specifically tailored to orographic mixed-phase clouds. Also on a global scale, aerosol variations were found to be key for understanding the variability of mixed-phase clouds (Atkinson et al., 2013). Recently, even the first closure studies bridging remote sensing observations of CCN and INPs with those of cloud droplet concentration and ice crystal number concentration were initiated (Ansmann et al., 2019; Engelmann et al., 2021). However, simulations and observational approaches have to date rarely been combined, which hinders one from drawing specific conclusions on aerosol effects on mixed-phase clouds. One key approach is to connect cloud-resolving, aerosol-sensitive ideally spectral bin models with forward operators in order to transfer simulation output into observation space. By doing so, simulations for selected scenarios can be evaluated against real-world observations. Given the complexity of spectral bin modeling frameworks, it is essential to incorporate the most relevant processes on the one hand and to constrain the environmental conditions to a maximum but still realistic state on the other hand. Besides number concentration, particle habit should thus also be incorporated into respective aerosol–cloud interaction studies which aim at a closure against observations. Ice particle shape plays a crucial role in determining the microphysical and radiative properties of mixed-phase and ice clouds (Mishchenko et al., 1996; McFarquhar and Heymsfield, 1997). The diverse shapes of ice particles influence their growth, aggregation, and riming processes, which in turn affect cloud lifetime, precipitation formation, and radiative energy transfer within the atmosphere (Magono and Lee, 1966; Heymsfield and Westbrook, 2010; Um and McFarquhar, 2011). The complexity and diversity of ice particle shapes present challenges for both cloud microphysics modeling and remote sensing of cloud properties. A comprehensive understanding of ice particle shape is essential for improving the accuracy of cloud microphysics models, remote sensing retrievals, and ultimately climate predictions (Liou and Ou, 2004; Tao et al., 2012; Chen and Liu, 2016; Vázquez-Martín et al., 2021). Despite its importance, the representation of ice particle shape in cloud microphysics models remains a significant challenge. Many models adopt simplified assumptions regarding ice particle shape, such as assuming all particles are spherical or using a limited set of predefined shapes (Mitchell, 1996; Morrison et al., 2005; Cotton et al., 2013). These simplifications can introduce uncertainties and biases in the simulated cloud properties and their interactions with radiation (Cotton et al., 2013). Furthermore, the complex nature of ice particle shape and its dependence on factors such as temperature, supersaturation, and aerosol loading add to the difficulty in accurately representing this aspect of cloud microphysics (Bailey and Hallett, 2009; Kanji et al., 2017). Radar remote sensing is a valuable tool for observing ice particles in clouds, providing insights into their size, shape, and spatial distribution (Hogan et al., 2000; Westbrook and Illingworth, 2011). However, interpreting radar observations of ice particles requires a thorough understanding of the reAtmos. Chem. Phys., 24, 5737–5756, 2024 https://doi.org/10.5194/acp-24-5737-2024 J. Lee et al.: Simulations of the impact of CCN and INP perturbations 5739 lationship between ice particle shape and the radar variables, such as reflectivity and Doppler velocity (Hogan et al., 2012; Kneifel et al., 2015). Radar forward simulators, which generate synthetic radar observations based on cloud model outputs, can help bridge this gap by allowing researchers to systematically investigate the sensitivity of radar variables to different ice particle shapes and model assumptions (Matsui et al., 2019). In this study, we utilize the Advanced Microphysics Prediction System (AMPS) coupled with the Kinematic Driver (KiD) to conduct idealized simulations of mixed-phase cloud microphysics (Hashino and Tripoli, 2007, 2008, 2011a, b), incorporating a comprehensive representation of ice particle shapes and the effects of CCN and INP perturbations. AMPS is a state-of-the-art cloud microphysics model that has been specifically designed to capture the complex interactions between aerosols, cloud droplets, and ice particles with a habit prediction system (Hashino and Tripoli, 2007, 2008, 2011a). The AMPS model coupled with largeeddy simulations (LESs) successfully reproduces features of mixed-phase clouds and has been compared to observations (Hashino et al., 2020; Ong et al., 2022). To investigate the impact of ice particle shape on radar retrievals, we employ the Passive and Active Microwave radiative TRAnsfer (PAMTRA) radar forward simulator. PAMTRA is a versatile tool that can simulate passive and active microwave observations of the atmosphere, accounting for the scattering properties of various ice particle shapes (Mech et al., 2020). By combining the capabilities of AMPS and PAMTRA, this study aims to provide a comprehensive understanding of the role of ice particle number size distribution and shape in mixedphase cloud microphysics and remote sensing retrievals under varying CCN and INP conditions. Furthermore, we seek to evaluate the impact of ice particle shape assumptions and CCN and INP perturbations on the accuracy and reliability of cloud property retrievals from radar observations. This paper is organized as follows. Section 2 briefly describes the Kinematic Driver (KiD) (Shipway and Hill, 2012) as the dynamical model and AMPS as the microphysics model. Section 3 gives information on the initial thermodynamic condition and experimental design for simulations. Section 4 shows the numerical simulation results for steadystate mixed-phase cloud cases under varying CCN and INP scenarios. Finally, Sect. 5 concludes the paper with a summary of the results. 2 Model description and simulation setup 2.1 KiD (dynamic model) The KiD model provides a framework for examining cloud microphysics, enabling us to assess and compare different parameterizations, which leads to a better understanding of cloud particle interactions and the influence of aerosols on cloud development. The model’s versatility allows its application in the study of various cloud types, including stratiform mixed-phase and convective clouds. It has significantly contributed to the enhancement of cloud microphysical parameterizations in larger-scale models (Klein et al., 2009; Shipway and Hill, 2012). The model accommodates a variety of microphysics schemes, from the simpler one-moment bulk models (Thompson et al., 2004) to more complex two-moment schemes (Thompson et al., 2008; Morrison et al., 2009; Shipway and Hill, 2012; Hill et al., 2015; Vié et al., 2016; Miltenberger et al., 2018). It is also compatible with detailed spectral bin microphysics schemes, including the Tel Aviv University bin microphysics and the AMPS (Tzivion et al., 1987, 1989; Hashino and Tripoli, 2007, 2008, 2011a, b; Lebo and Seinfeld, 2011; Onishi and Takahashi, 2012), as well as with Lagrangian cloud models (LCMs) (Andrejczuk et al., 2010; Arabas et al., 2015; Hoffmann et al., 2015; Dziekan et al., 2019). Further details on these aspects are provided in Shipway and Hill (2012) and Hill et al. (2023). We emphasize the KiD framework’s effectiveness in efficiently evaluating the performance of different microphysics schemes. Additionally, the KiD model functions as a valuable benchmarking tool, enabling researchers to evaluate and enhance cloud microphysics parameterizations. By comparing the results of various parameterizations within the KiD framework, inconsistencies and areas for improvement can be identified and investigated. 2.2 AMPS (microphysics model) In this study, we employed the Kinematic Driver (KiD) model in conjunction with the Advanced Microphysics Prediction System (AMPS) to simulate mixed-phase clouds. The AMPS model has been coupled with other dynamic models such as the University of Wisconsin Nonhydrostatic Modeling system (Hashino and Tripoli, 2007, 2008; Hashino et al., 2020) and the Scalable Computing for Advanced Library and Environment (SCALE) large-eddy simulation model (Ong et al., 2022), demonstrating its capability to accurately predict mixed-phase clouds and exhibiting favorable comparisons with observational data. According to Hashino and Tripoli (Hashino and Tripoli, 2007, 2008, 2011a, b), the AMPS microphysical model employs the Spectral Ice Habit Prediction System (SHIPS), which incorporates particle property variables (PPVs) to characterize the physical structure of ice particles, as detailed in Table 1. The SHIPS continuously updates the PPVs for each mass bin in response to evolving ambient conditions, ensuring accurate particle property diagnoses based on the PPVs. The identification of ice particle type and habit relies on various components, including mass content, length, and concentration. In our methodology, the SHIPS defines the ice particle model as a conceptual shape to represent ice particles leading to their genesis, encompassing “pristine cryshttps://doi.org/10.5194/acp-24-5737-2024 Atmos. Chem. Phys., 24, 5737–5756, 2024 5740 J. Lee et al.: Simulations of the impact of CCN and INP perturbations Table 1. List of the 16 particle property variables (PPVs) within liquid and ice spectra across each bin, as utilized in the KiD-AMPS model. Here, ρmdenotes the moist air density, while ρlat and ρiat specify the total aerosol density within the liquid phase and ice phase, respectively. Additionally, ρlas and ρias correspond to the soluble aerosol density in the liquid and ice phases. Spectrum PPV Description Liquid ρlat/ρmMixing ratio of total aerosol mass ρlas/ρmMixing ratio of soluble aerosol mass Ice ρcry/ρmMixing ratio of crystal mass ρrim/ρmMixing ratio of riming mass ρagg/ρmMixing ratio of aggregate mass ρfrz/ρmMixing ratio of frozen mass ρmlt/ρmMixing ratio of meltwater mass ρiat/ρmMixing ratio of total aerosol mass ρias/ρmMixing ratio of soluble aerosol mass nexice Extra crystalline structure number Vcs Circumscribing volume l3 aCube of the a-axis length l3 cCube of the c-axis length l3 dCube of the d-axis length (cube of the dendritic arm) agCenter of gravity along the aaxis cgCenter of gravity along the caxis tals, aggregates, rimed aggregates, graupel, and rimed crystals” as explicated in Fig. 2 by Hashino and Tripoli (2007). Pristine crystals, rimed crystals, aggregates, and rimed aggregates are modeled as cylinders, while graupel is represented as a spheroid. These shapes serve as the basis for determining the maximum dimension (D) of each particle. However, it is important to note that this diagnosis is primarily intended for comparison with observations or other models using predicted mass bin information within the PPVs. Therefore, additional errors may arise when artificially categorizing these types. In this study, we analyzed the mass bin information without separate type divisions for a more comprehensive assessment. The habit of ice crystals, a critical aspect of our study, is determined by analyzing their unique crystallographic properties, which include forms such as plates, dendrites, columns, and three polycrystals, as illustrated in Fig. 1. For each identified particle habit, the SHIPS within the AMPS model assigns an ice particle model that represents the geometric shape enveloping the ice particle. This model encompasses detailed crystal habit information – such as the a-axis length (la), representing the radius; the c-axis length (lc), representing the height; and the d-axis length (ld), representing the dendritic arm – across the three crystal habits of plate, columnar, and dendrite for monocrystals. Additionally, the model employs a PPV, termed the extra crystalline structure number (nexice), which ranges from 0 to 1. A value of nexice greater than or equal to 0.5 signifies that the ice crystals in a particular bin are polycrystals. The SHIPS’s methodical approach also integrates the coordinates of the center of gravity (ag,cg), measured along the aand caxes from the center of the monocrystals, as distinct PPVs. These measurements are pivotal in differentiating between planar and columnar polycrystals: a planar polycrystal is identified if the ratio ag/la exceeds the ratio cg/lcby more than 0.5, whereas a columnar polycrystal is determined if cg/lcexceeds ag/laby more than 0.5. Ice crystals that do not fit within these criteria, such as scale-like side planes, are categorized as irregular polycrystals. The process and criteria for habit diagnosis are further detailed in Fig. 2. To explain more about ice particle habit, the ice crystal growth regime is determined based on the cumulative relative frequency of habits and a random number generator when the temperature falls below −20 °C and the maximum dimension of the crystal is less than 20 µm. The growth regime is initially selected from polycrystalline, columnar, and planar hexagonal regimes. If the growth regime is polycrystalline, a random number determines whether it is columnar or planar polycrystalline. Furthermore, if it is polycrystalline, another random number determines the growth regime for a hexagonal monocrystal. This implies that small ice crystals can grow differently from the habit diagnosed at the beginning of the time step. Conversely, for temperatures above −20 °C, it is assumed that polycrystals do not form. Once the maximum dimension exceeds 20 µm, the ice crystal is presumed to follow the growth of the diagnosed habit at the beginning of the time step. Expanding upon this, the concept of AR of an object is the ratio of its width to its height. In this study, the AR of polycrystalline ice particles is determined by the ratio of the semiaxis lengths of the ice particle model, i.e., α=lc,sm/la,sm, while the AR of monocrystalline particles is determined by the ratio of the axis lengths, α=lc/la. Atmos. Chem. Phys., 24, 5737–5756, 2024 https://doi.org/10.5194/acp-24-5737-2024 J. Lee et al.: Simulations of the impact of CCN and INP perturbations 5741 Figure 1. Diagnosis of the habit of the representative hydrometeor dimensions (la,lc, and ld) for monocrystals, including (a) hexagonal plate, (b) column, and (c) dendrite. Figure 2. Flowchart depicting the diagnostic procedure for identifying ice particle habits in the AMPS. Within the framework of the AMPS model, ice particles are characterized by their circumscribing sphere volume, denoted as Vcs. This volume is pivotal for comprehending the microphysical behavior of ice particles within mixed-phase clouds. As Hashino and Tripoli (2011a) explain, the circumscribing sphere volume of an ice particle is instrumental in forecasting the mass–dimension (m–D) relationship. The AMPS model utilizes functions that interlink the predicted sphere volume with the diagnosed aspect ratios, semiaxis lengths, and the particle’s maximal extent. Each mass bin’s circumscribing sphere volume, a key PPV, is integral to these prognostications. The model assumes a consistent geometric form across microphysical processes, transferring the concentration-weighted circumscribing sphere volume between mass bins as per the collection process. This transfer is essential to ascertain the circumscribing sphere volume of representative hydrometeors, thereby ensuring an accurate representation of the ice particles’ physical properties. Moreover, this factor critically influences the determination of the effective diameter. The formulation for calculating Vcs depends on the particle type: for most ice particles, it is determined by the equation ks(la,sm)3(1+α2)3/2assuming a cylinder, where ks=4π/3, and αrepresents the aspect ratio (AR) of the ice particle defined as the ratio of the vertical semiaxis length (lc,sm) to the horizontal semiaxis length (la,sm). However, for graupel, it is computed using ks(la,sm)3max(1,α3) assuming an ellipsoid. It is important to highlight that while categorizing solid hydrometeors into specific types and habits is not obligatory for conducting SHIPS microphysics simulations, it greatly improves the model verification process. This is especially valuable because observational data are frequently organized based on these conventional classifications, facilitating more robust model comparisons. Given this capability, we conclude that AMPS is well suited to investigate the impact of varying CCN and INP conditions on particle shape. https://doi.org/10.5194/acp-24-5737-2024 Atmos. Chem. Phys., 24, 5737–5756, 2024 5742 J. Lee et al.: Simulations of the impact of CCN and INP perturbations 2.3 PAMTRA (radar forward simulator) The equivalent radar reflectivity factor denoted as Ze, characterizes the collective scattering properties of a volume of scatterers, such as atmospheric precipitation particles, rather than just a single object. This factor is crucial in identifying and quantifying precipitation events by enabling the detection of radar signal reflections from these particles. The magnitude of Zeis influenced by several factors: the size and concentration of the precipitation particles, their composition, and the frequency of the radar signal. To convert the model data into radar variables, we utilize PAMTRA, a powerful tool designed for simulating and retrieving microwave radiative properties in the atmosphere. Serving as a forward model, PAMTRA allows the interpretation of data from diverse passive and active microwave sensors, including radars and radiometers, used for observing and studying precipitation and other atmospheric phenomena. PAMTRA incorporates crucial factors such as cloud and precipitation scattering as well as gas absorption and facilitates comprehensive analysis of remote sensing data from satellite, airborne, and ground-based platforms. Numerous studies have leveraged PAMTRA to investigate precipitation processes, cloud microphysics, and remote sensing of atmospheric variables, establishing it as an invaluable resource for researchers and atmospheric scientists engaged in the field of radiative transfer and remote sensing (Maahn et al., 2019; Ori et al., 2020; Schnitt et al., 2020; von Lerber et al., 2022). PAMTRA offers a full-bin interface with several benefits, which can directly convert spectral bin model data or in situ measurement for each size bin to radar variables. This conversion facilitates the transfer of crucial information, such as mass, density, number concentration, terminal velocity, cross-sectional area, AR, and particle size distribution, without the need for further assumptions. In contrast, a bulk microphysics model provides hydrometeor mixing ratio in onemoment microphysics models (e.g., Baldauf et al., 2011) or mixing ratio and number concentration in two-moment microphysics models (e.g., Seifert and Beheng, 2006), requiring assumptions about particle size distribution, mass–size relations, and terminal velocity in PAMTRA. In the context of the spectral bin model AMPS, the terminal velocity for the sedimentation of a prognostic variable for a specific bin is determined by considering the mass, concentration, and the particular type of PPVs within that bin (Hashino and Tripoli, 2007). Radar reflectivity factor ze(mm6m−3) is obtained by integrating the normalized particle size distribution (PSD) n(D) over the entire range of particle sizes D, ze=Z1018σB(D)n(D)λ4 π5|Kw|2dD, (1) where λis the wavelength in meters, |Kw|corresponds to the dielectric factor of water, and σB(D) stands for the backscattering cross-section of individual hydrometeor particles in square meters (m2). Typically, radar reflectivity is used in logarithmic units converted with Ze(dBZ) = 10log10ze(mm6m−3). It is standard practice to use the value for liquid water at centimeter wavelengths (|Kw| = 0.93 at Ka-band; Ulaby et al., 1981) regardless of whether ice or liquid clouds are observed. However, |Kw|is also frequency dependent. This study employs the self-similar Rayleigh–Gans approximation (SSRGA) parameterization proposed by Hogan et al. (2017) for the backscattering cross-section σB(D) calculation. This parameterization is determined by five dimensionless parameters: αe,K,β,γ, and ζ. The AR of the particles is represented by αe, whereas Kmeasures the mean mass distribution of the particle along the propagation direction and is referred to as the kurtosis parameter. The mass fluctuations around the mean mass distribution are described by βand γ, which represent the power law prefactor and exponent, respectively. ζis a correction term for the power spectrum of the smallest wavenumber. We choose the SSRGA coefficients depending on the normalized particle rime mass fraction following Maherndl et al. (2023). 3 Model description and simulation setup 3.1 Initial profile We selected a one-dimensional mixed-phase stratocumulus case for our experiments with the KiD-AMPS framework. To minimize the effects of the microphysics schemes, the temperature field is kept constant. The vertical velocity demonstrates repeated up–down oscillation, and an additional vapor source is supplied to artificially recreate a quasi-steady stratocumulus condition. The vertical velocity is given as w(z,t)=   w1z z11−exp−z−z1 z22sin(πt/t1),if z < z1 0.0,otherwise (2) and the additional forcing dq dtadd (z,t)=dq dtadd (z,0) =       Acos1.25π 2,if z < z3+z4, Acosz−z3 z4 π 2,if z3+z4< z < z3 +1.25z4, (3) and Asatisfies z5 Z 0 dq dtadd(z,0)dz=fq/3600.(4) The parameters z1,z2,z3,z4, and z5are set to 450, 200, 400, 100, and 1000 m, respectively. Similarly, the values of ω1,t1, and fqare set to 1.0 m s−1, 600 s, and 5 mm h−1, respectively. The initial potential temperature and specific humidity profile are displayed in Fig. 3. This implies that the cloud’s top region exhibits a temperature of approximately −20 °C, whereas the lowermost part of the cloud shows a temperature of −15 °C. Importantly, the profiles were specifically crafted to emulate a mixed-phase stratocumulus layer, Atmos. Chem. Phys., 24, 5737–5756, 2024 https://doi.org/10.5194/acp-24-5737-2024 J. Lee et al.: Simulations of the impact of CCN and INP perturbations 5743 Figure 3. The initial condition of the (a) potential temperature θ (K) and (b) specific humidity qv(g kg−1) profile in the KiD-AMPS simulation. drawing inspiration from the Global Energy and Water Cycle Experiment Cloud System Study (GCSS) Surface Heat Budget of the Arctic Ocean (SHEBA) intercomparison, a choice substantiated by previous studies (Klein et al., 2009; Morrison et al., 2011; Fridlind et al., 2012). This selection was made with the deliberate intent of aligning our study’s environmental conditions with well-established benchmarks, thereby enabling seamless comparisons with other investigations centered around similar Arctic settings. The GCSS– SHEBA intercomparison is widely acknowledged for providing meticulously documented atmospheric conditions that faithfully represent Arctic stratocumulus clouds, rendering it an ideal resource for our initial profiles. 3.2 Experimental designs (CCN and INPs) This study aimed to investigate the impact of varying initial concentrations of CCN and INPs on the formation and evolution of clouds. It is widely recognized that aerosols can significantly influence cloud formation and evolution, with their role in these processes depending on factors such as size, composition, and concentration. It should be noted that while aerosols can act as a source of INPs, not all aerosols possess this capability. INPs are particular types of particles capable of initiating ice crystal formation at temperatures below 0 °C. The precise composition and physical properties of INPs can considerably vary based on specific conditions, with mineral dust, biological particles (e.g., bacteria and fungi), and certain anthropogenic particles (e.g., industrial emissions) being the most prevalent sources of INPs (Hoose and Möhler, 2012). For this research, we employ ammonium sulfate as CCN and assume that the insoluble portion of all aerosols consists of montmorillonite as INP sources. Hashino et al. (2020) demonstrated that the volume-dependent Bigg’s immersion method (Diehl and Wurzler, 2004) is well suited for characterizing the ice nucleation process. Consequently, we adopt Bigg’s immersion freezing method in this study as well. For further details on ice nucleation schemes, de Boer et al. (2010, 2013) and Hashino et al. (2020) can be referred to. Within the AMPS model, aerosol particles are represented by lognormal size distributions, which provide comprehensive coverage of their size range. In the CCN activation scheme, the particle size distribution is divided into 10 bins, and for each bin, the critical supersaturation value is individually computed. As the iteration proceeds, bins that exhibit a critical supersaturation below the threshold of environmental supersaturation are identified and subsequently transferred to the liquid spectrum. This methodology ensures the appropriate incorporation of bins in the liquid phase, leading to a faithful depiction of cloud microphysics within the AMPS model. As previously mentioned, montmorillonite particle number concentration serves as an INP proxy for this simulation. For the present study, we conduct sensitivity analyses in this study by altering CCN and INP proxy concentrations across an extensive range, from extremely low to exceptionally high levels, as illustrated in Table 2. The initial CCN concentrations for these sensitivity simulations are set at 10, 50, 500, 1000, and 5000 cm−3(denoted as CCN10, CCN50, CCN500, CCN1000, and CCN5000, respectively). For each CCN condition, simulations are carried out with initial INP particle concentrations of 0.001, 0.1, and 10 L−1, respectively, labeled INP0.001, INP0.1, and INP10. A similar range for the experimental setup was also selected in the sensitivity study of Fan et al. (2017). The authors further emphasize that in heavily polluted regions like China and India, where CCN concentrations exceeding 1000 cm−3are prevalent, such high values hold significant implications for precipitation extremes and water cycles. Choudhury and Tesche (2023) provide a comprehensive global multiyear dataset of height-resolved concentrations of cloud condensation nuclei (CCN) categorized by aerosol types. These estimates are derived from the spaceborne lidar instrument aboard the CloudAerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite. Notably, recent studies have demonstrated that the levels of extreme CCN concentrations in heavily polluted regions can exceed 5000 cm−3. The selected INP range is well motivated with respect to the variability that can be found in existing in situ and remote sensing studies of INP concentrations. Specifically, we based the decision for the lowest value of INP concentration on the values reported for the free troposphere over the Southern Ocean site of Punta Arenas, Chile (Radenz et al., 2021). The selected maximum value of INP concentration of 10 L−1can, e.g., be observed in the case of strong Saharan dust outbreaks in the Mediterranean region, as reported for instance by Ansmann et al. (2019). The range also agrees well with in situ measurements, as reported by DeMott et al. (2010). In total, we conducted 15 experiments, and from this set, we specifically selected five representative cases (EXP1–5) for detailed analysis in Sect. 4.1 of the Results section (see Table 2). These selected cases encompass clean, pristine, and polluted scenarios, achieved by varying the concentration of https://doi.org/10.5194/acp-24-5737-2024 Atmos. Chem. Phys., 24, 5737–5756, 2024 5744 J. Lee et al.: Simulations of the impact of CCN and INP perturbations Table 2. Definition of the EXP1–5 experiments referred to in the remainder of this study. The simulations are conducted using various concentrations of CCN and INP aerosols. CCN [cm−3] 10 50 500 1000 5000 0.001 X EXP3 X X X INP [L−1] 0.1 EXP4 EXP2 EXP5 X X 10 X EXP1 X X X CCN and adjusting the concentration of INPs to be 100 times smaller and 100 times larger than the commonly observed value of 0.1 L−1in Arctic mixed-phase clouds. The results of the sensitivity test, covering all cases, are presented in Sect. 4.2. Additionally, Sect 4.3 showcases the outcomes of coupling typical EXP1–5 cases with PAMTRA for radar retrieval analysis. 4 Results 4.1 Comparative analysis of CCN and INP effects on mixed-phase clouds We analyze the evolution of cloud formations and the progression of hydrometeors by altering the initial CCN and INP concentrations. In this section, our primary focus is on cases EXP1–5 (see Table 1), which represent the most contrasting experimental scenarios among the 15 cases examined. Figure 4 presents the temporal evolution of cloud development, specifically focusing on the average column value over time. This figure provides valuable insights into the progression of cloud formation in relation to the initial conditions, considering the variations in CCN and INPs. In Fig. 4a, we present the effective diameter values of ice particles obtained from the simulations. In this study, the effective diameter of ice particles, denoted as Deff,ice, is calculated using Eq. (5). Deff,ice =2pVcs/(πlc,sm) (5) The mean effective diameters after t=100 min for EXP1, EXP2, EXP3, EXP4, and EXP5 are 79, 293, 331, 275, and 346 µm, respectively. It is observed that as the concentration of INPs increases, there is a corresponding decrease in the effective diameter of ice particles, as evident in EXP1, EXP2, and EXP3. Conversely, an increase in CCN concentration results in a slight increase in the effective diameter, as observed in EXP4 and EXP5. The simulated effective diameters of ice particles span a wide range, ranging from small values in the tens of micrometers to larger values in the range of thousands of micrometers. These findings are consistent with previous observations indicating effective diameters within the range of 300 to 800 µm (Morrison et al., 2011). AMPS is comprised of two separate bin spectra. The first 40 bins represent the liquid phase and are categorized as eiFigure 4. Time series of key variables: (a) effective diameter of ice crystals (Deff,ice) with the dotted line representing the average value from 100 min, (b) ice number concentration (Nice), (c) liquid number concentration (Nliq), (d) mixing ratio of ice (mice), and (e) mixing ratio of liquid (mliq). The gray-shaded regions correspond to the ranges derived from observational data obtained during the SHEBA campaign. ther cloud or rain, while the next 20 bins represent the ice phase. The mixing ratio and number concentration of liquid and ice particles are found to remain in a quasi-steady state, with the exception of EXP1. Moreover, the experimental results in Fig. 4b for the number concentration of ice (Nice) in EXP2, EXP4, and EXP5, ranging from 0.3 to 1.7 L−1(gray zone), are in agreement with previous observations from the SHEBA campaign of mixed-phase clouds (Morrison et al., 2011; Fridlind et al., 2012). These results further suggest that the experiment accurately represents typical mixed-phase clouds. Figure 4 indicates that as INP concentrations increase in EXP1, EXP2, and EXP3, both Nice and mice significantly increase, while Nliq and mliq decrease. Conversely, the mean mice is slightly higher in experiments with increased CCN (EXP4, EXP2, and EXP5) despite the higher Nliq and mliq, consistent with findings from earlier studies employing two-moment bulk microphysics simulations (Solomon et al., 2018) and experimental investigations (Desai et al., 2019). The impact of CCN and INP perturbations on cloud evolution is illustrated in Fig. 5. It is observed that increasing CCN concentration can significantly affect the relationship between liquid water and ice mixing ratios. In response to the increased concentration of INPs, the ice water content increased, as well. In the high-INP scenario (EXP1), the conAtmos. Chem. Phys., 24, 5737–5756, 2024 https://doi.org/10.5194/acp-24-5737-2024 J. Lee et al.: Simulations of the impact of CCN and INP perturbations 5745 Figure 5. Vertical distribution comparison of (a) liquid water content (LWC) (g kg−1) and (b) ice water content (IWC) ( kg−1) across EXP1–5. version of water droplets into ice crystals occurs more rapidly and efficiently. Consequently, this case predicts almost fully glaciated clouds. When the number of CCN increases, more cloud droplets are formed from within the reservoir of available water vapor. This can lead to an increase in the number concentration of cloud droplets within the cloud as shown in Fig. 4c, which can, in turn, reduce the size of individual cloud droplets. This reduction in droplet size increases the altitude of the mixed-phase cloud base and reduces the amount of precipitation, which can result in an increase in cloud water mass, given that the cloud is not already saturated with the available water vapor. In summary, the alteration of cloud particle concentration due to perturbations in the aerosol concentrations leads to adjustments in cloud and precipitation patterns, even in the absence of cloud– dynamics interaction, as previously observed in studies by Seifert et al. (2012), Boucher et al. (2013), Heyn et al. (2017), Possner et al. (2017), Solomon et al. (2018), and Zhang et al. (2018). Next, we investigate the response of the ice-phase processes of AMPS to the aerosol perturbations. In the AMPS model, the mass of an ice crystal is partitioned into various process-oriented categories, including pristine crystal mass, aggregated mass, riming mass, and melted water mass, which are tracked as PPVs. The bin components are created through microphysical processes that act upon them, including vapor deposition onto ice crystals, which produce ice crystal mass; melting processes, which produce liquid mass; aggregation processes, which generate aggregate mass; and riming processes, which produce rime mass. The sum of these Figure 6. Temporal evolution comparison of the mean mixing ratios of ice processes with the dotted line representing the average value from 100 min, including melting, riming, aggregation, and crystal across EXP1–5. The aggregation process is abbreviated as “agg.”. https://doi.org/10.5194/acp-24-5737-2024 Atmos. Chem. Phys., 24, 5737–5756, 2024 5752 J. Lee et al.: Simulations of the impact of CCN and INP perturbations tive feedbacks, which play a significant role in cloud layer heating, cooling, and their impact on cloud microphysics and dynamics. Neglecting these processes could lead to inaccuracies in the representation of cloud temperature, water vapor distribution, and vertical motions within mixed-phase clouds. To address these limitations, future research will explore shape-integrated simulation model AMPS coupled with three-dimensional dynamic cores such as the ICOsahedral Non-hydrostatic (ICON; Zängl et al., 2015) modeling framework. The simulation data can then be compared to observational radar data using a radar forward simulator, selecting an observation dataset from field experiments. By incorporating more sophisticated tools and remote sensing techniques, a more comprehensive and accurate analysis of cloud behavior can be achieved. The aim is to enhance cloud simulations by incorporating these processes to improve realism and accuracy. Furthermore, there is a pressing need for future experiments that address a more detailed distribution of INP and CCN perturbations. Such studies aim to deepen our understanding of the dynamics of aerosol–cloud interactions. This entails evaluating how aerosol perturbations influence the evolution of idealized stratiform mixed-phase clouds, a necessary step before a precise general assessment of aerosol– cloud interactions can be realized. Such efforts will be instrumental in improving the realism and accuracy of cloud simulations by incorporating sophisticated modeling and remote sensing techniques, thereby enhancing our ability to predict cloud behavior and its implications for the climate accurately. Code and data availability. The base code for the KiD model is publicly accessible via GitHub (https://github.com/Adehill/KiD-A. git, Hill, 2023). The AMPS codes used in this study can be obtained from the corresponding author. Simulation results are available at https://doi.org/10.5281/zenodo.8257078 (Lee et al., 2023). Author contributions. JL conducted all simulations, analyzed the data, and wrote the manuscript draft. JL, PS, and OK contributed initial ideas and designed experiments. PS and TH supervised the work and revised the paper. TH provided the AMPS, MM provided the PAMTRA code, and both provided support throughout the work. All authors actively collaborated in the development of the paper and participated in scientific discussions. Competing interests. The contact author has declared that none of the authors has any competing interests. Disclaimer. Publisher’s note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Special issue statement. This article is part of the special issue “Fusion of radar polarimetry and numerical atmospheric modelling towards an improved understanding of cloud and precipitation processes (ACP/AMT/GMD inter-journal SI)”. It is not associated with a conference. Acknowledgements. We acknowledge the support of the DFG priority program SPP 2115 PROM (project no. 359922472) and the provision of additional funding by SPP 2115 for a research stay of Junghwa Lee in Japan, which enabled the initial cooperation with Tempei Hashino, the key developer of AMPS, for the provision of training in using AMPS. We thank Tempei Hashino for hosting Junghwa Lee at his institution. Financial support. This research has been supported by the Deutsche Forschungsgemeinschaft (grant no. 40802749), the Japan Society for the Promotion of Science Grant-in-Aid for Scientific Research (C) (grant no. JP21K03665), and by the European Commission (grant no. 101137639). 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